Base code

backend/services/persona/README.md

@@ -0,0 +1,106 @@
+# Persona Services Package
+
+This package contains platform-specific persona generation and analysis services, providing a modular and extensible architecture for creating platform-optimized writing personas.
+
+## Structure
+
+```
+services/persona/
+├── __init__.py                    # Package initialization
+├── linkedin/                      # LinkedIn-specific persona services
+│   ├── __init__.py               # LinkedIn package initialization
+│   ├── linkedin_persona_service.py    # Main LinkedIn persona service
+│   ├── linkedin_persona_prompts.py    # LinkedIn-specific prompts
+│   └── linkedin_persona_schemas.py    # LinkedIn-specific schemas
+└── README.md                     # This documentation
+```
+
+## LinkedIn Persona Services
+
+### LinkedInPersonaService
+The main service class for generating LinkedIn-specific persona adaptations.
+
+**Key Features:**
+- Enhanced LinkedIn-specific prompt generation
+- Professional networking optimization
+- Industry-specific adaptations
+- Algorithm optimization for LinkedIn
+- Persona validation and quality scoring
+
+**Methods:**
+- `generate_linkedin_persona()` - Generate LinkedIn-optimized persona
+- `validate_linkedin_persona()` - Validate persona data quality
+- `optimize_for_linkedin_algorithm()` - Algorithm-specific optimizations
+- `get_linkedin_constraints()` - Get LinkedIn platform constraints
+
+### LinkedInPersonaPrompts
+Handles LinkedIn-specific prompt generation with professional optimization.
+
+**Key Features:**
+- Industry-specific targeting (technology, business, etc.)
+- Professional networking focus
+- Thought leadership positioning
+- B2B optimization
+- LinkedIn algorithm awareness
+
+### LinkedInPersonaSchemas
+Defines LinkedIn-specific JSON schemas for persona generation.
+
+**Key Features:**
+- Enhanced LinkedIn schema with professional fields
+- Algorithm optimization fields
+- Professional networking elements
+- LinkedIn feature-specific adaptations
+
+## Usage
+
+```python
+from services.persona.linkedin.linkedin_persona_service import LinkedInPersonaService
+
+# Initialize the service
+linkedin_service = LinkedInPersonaService()
+
+# Generate LinkedIn persona
+linkedin_persona = linkedin_service.generate_linkedin_persona(
+    core_persona=core_persona_data,
+    onboarding_data=onboarding_data
+)
+
+# Validate persona quality
+validation_results = linkedin_service.validate_linkedin_persona(linkedin_persona)
+
+# Optimize for LinkedIn algorithm
+optimized_persona = linkedin_service.optimize_for_linkedin_algorithm(linkedin_persona)
+```
+
+## Integration with Main Persona Service
+
+The main `PersonaAnalysisService` automatically uses the LinkedIn service when generating LinkedIn personas:
+
+```python
+# In PersonaAnalysisService._generate_single_platform_persona()
+if platform.lower() == "linkedin":
+    return self.linkedin_service.generate_linkedin_persona(core_persona, onboarding_data)
+```
+
+## Benefits of This Architecture
+
+1. **Modularity**: Each platform has its own dedicated service
+2. **Extensibility**: Easy to add new platforms (Facebook, Instagram, etc.)
+3. **Maintainability**: Platform-specific logic is isolated
+4. **Testability**: Each service can be tested independently
+5. **Reusability**: Services can be used across different parts of the application
+
+## Future Extensions
+
+This architecture makes it easy to add new platform-specific services:
+
+- `services/persona/facebook/` - Facebook-specific persona services
+- `services/persona/instagram/` - Instagram-specific persona services
+- `services/persona/twitter/` - Twitter-specific persona services
+- `services/persona/blog/` - Blog-specific persona services
+
+Each platform service would follow the same pattern:
+- `{platform}_persona_service.py` - Main service class
+- `{platform}_persona_prompts.py` - Platform-specific prompts
+- `{platform}_persona_schemas.py` - Platform-specific schemas